11 minute read
Eyes on the world
Public agencies around the world are dealing with new and difficult problems. PETER MCKENZIE examines two articles that suggest new ways of dealing with challenges like pandemics and climate change.
As it responds to huge problems like climate change and COVID-19, Aotearoa is entering a new era of governance – one marked by larger and more complicated problems that demand new and more innovative solutions. To face these challenges, the public service will have to adapt to new methods of working across categories and hierarchies.
This Eyes on the World will focus on two articles that deal with this challenge. The first, “The Case for Mesh Governance” by Geoff Mulgan at University College London pushes back on the impulse to centralise and examines how different levels of government can co-operate across hierarchies. The second, “Think Tanks: New Organisational Actors in a Changing Swedish Civil Society” by Pelle Åberg, Stefan Einarsson, and Marta Reuter in Voluntas gives an insight into how civil society actors external to government can and should be integrated into the policy process.
“The Case for Mesh Governance” – Geoff Mulgan
COVID-19 has put intense pressure on governments around the world. Aside from the tragic loss of life, the pandemic has also acted as a natural experiment in best-practice governance. We can learn from the variety of governmental responses in order to perform better when confronted by similar challenges. There is, however, controversy about which lessons we should learn. Advocates of centralised government point to our experiences in Aotearoa New Zealand as proof that centralisation ensures efficiency and clarity. Meanwhile, advocates of decentralisation underline how important the semi-autonomy of states and cities has been in the United States, where clear federal co-ordination has been sorely lacking.
Mulgan argues that neither of these impulses is correct. Instead, the most successful responses to COVID-19 have been marked not by an entrenchment of hierarchy but by a willingness to work across it. He calls this “mesh governance”, which he defines as “an integration of multiple tiers, acting together, sharing data, lessons and insights.” Physical mesh combines vertical and horizontal links in order to make a system (whether in fabric or in a computer-based system) stronger.
He points to a number of examples of mesh governance, such as South Korea’s Central Crisis Management Committee (which is composed of representatives from both national ministries and large cities), Australia’s Council of Australian Governments (which brings together both national and state governments), and the UK’s now-defunct Government Regional Offices (which performed a similar function with different regions and cities).
According to Mulgan, mesh governance has a number of key features:
1. Support for relationships and networks – the central goal of mesh governance is not merely to establish meetings where different tiers of government are represented, but to foster
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genuinely trusting relationships between those different tiers. If individuals and groups from each tier can engage with each other and build informal networks, then it will be easier to co-ordinate formal machinery across hierarchies.
Co-ordinated vision and problem formation – in order to coordinate policy responses, it is crucial to first agree on what the problem being confronted is and what is likely to come next. With that shared vision, solution-oriented policy making becomes much smoother.
Combined problem-solving teams – having generated a shared vision, problem-solving teams composed of officials from across the hierarchy can build customised policy responses that draw on expertise from each tier and so are more likely to receive buy-in.
Integration of other civil society actors – mesh governance should engage actors from across civil society. Universities, think tanks, and advocacy organisations will also have invaluable expertise and experience, which they can contribute to ease the policy-creation and implementation process.
Combined curation of data – a difficulty for co-ordinated government responses is that the actors involved are often operating with different sources of information that occasionally point in different ways. Joint curation of data allows a more comprehensive and accurate data picture, which all actors can draw from together.
The merits of such an approach have already been seeded in most governments through the creation of joint task forces (often in a security or emergency context, like COBRA in the United Kingdom or the cross-agency co-operation found in Aotearoa’s National Crisis Management Centre). Mulgan argues it is now time for governments to build that co-operation not just across, but down.
Too much collaboration can be deeply damaging to productivity. But at an institutional level, we are still far from that point. Different tiers of government act in ways that are at best additive. Often they are contradictory. According to Mulgan, “With a good mesh structure in place, they can become multiplicative, becoming more than the sum of their parts.”
“Think Tanks: New Organisational Actors in a Changing Swedish Civil Society” – Pelle Åberg, Stefan Einarsson, and Marta Reuter
The process of policy creation is constantly evolving to reflect new societal and political trends. The most important evolutions have centred around the actors involved in policy creation; instead of being controlled by one government entity with subject-matter expertise, policy creation is increasingly a competitive space with multiple government entities, charities, and advocacy groups involved.
This trend of diversification in the area of policy creation will and should continue as Aotearoa deals with the problems that now dominate the public agenda. That is especially true for an increasingly important civil society actor: the think tank. These technocratic advocacy groups are more and more common in Aotearoa; among them are the Institute for Governance and Policy Studies, the New Zealand Initiative, the Salvation Army’s Social Policy & Parliamentary Unit, and New Zealand Alternative.
It is important that the public service better understand the origin and role of these advocacy groups, so that it can better integrate their expertise and perspective into the policy-creation process. It is here that the work of Åberg, Einarsson, and Reuter is relevant. They surveyed the growing think-tank ecosystem in Sweden’s highly mature civil society, which until recently has been dominated by large mass-membership actors. Of the 38 identifiable Swedish think tanks, 29 have been launched since 2000. They operated on the assumption that if the proliferation of think tanks could happen there, “it can happen anywhere”.
This proliferation, according to Åberg, Einarsson, and Reuter, is the result of three trends: first, the changing role of popular movements – as political parties and mass-membership organisations (such as the labour movement) have transitioned away from a focus on policy, think tanks have emerged to take their place; second, a shift in public discourse – ideological visions have faded in perceived legitimacy in comparison with the more evidence-based and technocratic approach, which think tanks tend to focus on; third, the evolving nature of political communication – in an environment defined by hourly or daily news cycles, political parties and mass-membership organisations have had to shift resources towards public communication and solicit policy from external actors like think tanks.
Åberg, Einarsson, and Reuter hypothesise that two societal factors determine the nature of these rapidly multiplying think tanks: a civil society’s liberal and social-democratic nature and the government’s pluralist and corporatist structure. Socialdemocratic regimes allow civil society to be predominantly advocacy oriented, in contrast with more liberal regimes that require civil society to be predominantly service oriented to make up for the shortfall in government welfare and support. Pluralist governments provide access to the policy-creation process for a range of civil society actors without favouring any in particular, whereas corporatist governments closely engage with a select few actors.
Aotearoa’s liberal-corporatist regime means that our civil society actors are largely service oriented and often locked out of the policy-creation process – a good example of this is the Salvation Army’s Social Policy & Parliamentary Unit. Given the extensive experience in service delivery that these civil society actors hold, their absence from the policy creation process means that the public service misses out on invaluable expertise and perspectives. Recognising this allows the public service to open up its corporatist structure to think tanks and engage with these increasingly important actors.
Conclusion
To address complicated and multi-faceted policy problems, the public service has to shift towards more effective and diverse methods of developing policy. The insights provided above – of shifting towards methods of mesh governance, which bridge governmental hierarchies, and opening up the policy-creation process to new and multiplying civil society actors like think tanks – offer a few answers to that emerging challenge.
IN THE PUBLIC SERVICE? ARTIFICIAL INTELLIGENCE AND GOVERNMENT
Artificial intelligence presents some marvellous opportunities for the public service. SEAN AUDAIN from Wellington City Council gives a summary of some of these along with the unique challenges.
The digital experience
The human experience is increasingly a digital one. This digital reality touches our lives in a myriad of ways, from conscious actions like electronic transactions and streaming entertainment services to the more unconscious ones such as dynamic traffic management or Google searches. This digital experience is reshaping the expectations people have of their public services. Of the many strands that make up this digital reality, few are as hyped, misunderstood, or promising as artificial intelligence (AI). This article gives a very brief description of AI, it explores how it fits with other technologies that will reshape the way government operates, and it identifies what public servants should consider in developing and growing this capability.
The thinking machine
At the core of AI is the idea that people can build and train machines that can apply the autonomy, intelligence, and decisionmaking processes we use to perform tasks and respond to situations. These machines take the form of algorithms, sets of rules and equations written for a computer and then applied to sets of data. While the common image of AI is a robot, modern robotics is a distinct discipline and the vast majority of AI is operated within computers or devices. As AI has been developed, two major categories have evolved: General AI and Narrow AI.
General AI – General AI is the AI of films and the public imagination – it is a synthetic intelligence that is recognisably human. This type of AI sees machines being able to display traits like abstract thinking, learning, reasoning, creativity, morality, emotional intelligence, and dealing with random occurrences. In short, General AI is equivalent to having an artificial consciousness. that help produce weather reports, that are the computer opponent in our video games, or that count cars and bicycles in our streets. Narrow AI is exceptionally good at doing specific things, using a dataset of a particular type. Narrow AI is excellent at repetitive tasks that would fatigue, bore, or annoy a person trying to do them. Given that Narrow AI is the AI in commercial use today, this article will focus on Narrow AI.
To develop these artificial intelligences, people generally use two techniques: machine learning and deep learning. Machine learning is essentially training an algorithm to perform a task, for example, using recordings of breaking glass to teach an audio algorithm to recognise breaking glass in the street so cleaners can be sent out to clear it away. Machine learning comes in a number of variants depending on the nature of the training or the algorithm being used. Deep Learning is more complex. It seeks to mimic the way our neural systems work. Deep Learning takes the linear processes of machine learning and weaves them together to make webs that can support selflearning and basic reasoning.
The difference between machine learning and deep learning are important for public servants to understand because they have profoundly different ethical, transparency, and democratic decision-making considerations.
The art of the possible
Government is an increasingly digital art, with almost every task from communication to application assessment, budget construction, and regulatory production having a digital dimension. As a tool, AI offers opportunities to free public servants to deliver better, more personal services and deliver more timely insights. AI has a number of potential advantages:
Automation – There are tasks in the public service that are so mechanical, dangerous, remote, or tedious that they are often either not done or not done well. Generally tasks that involve repetition, counting, or limited decision making are suitable for automation. Examples already in use include understanding pest trapping metrics on offshore islands, counting swimming pools from aerial photographs for water-use planning, or counting different types of vehicles in road traffic.
Personalisation – The personalisation that has helped make Google and Netflix the service leaders in their industries are also increasingly expected from public services. Chat bots can be used in application processes. This can make government services more accessible to a greater diversity of people and more effectively allow people to use public systems. An example of this is Better Rules, which is operated through Ministry of Business, Innovation and
